The present invention relates to an interference signal cancelling method and a receiver using the same and, more particularly, to an interference signal cancelling method which compensates for the degradation of transmission characteristics due to co-channel or similar interference signals from other adjacent cells in digital mobile radio communication and a receiver and a communication system using such an interference signal cancelling method.
There have already been proposed several types of receivers that generate replicas from transmission symbol candidates for desired and interference signals and transmission line parameters corresponding to these two signals, subtract these replicas from a received signal to obtain an error signal, multiply the square of the error signal by -1 and use the resulting signal as a log likelihood to make a maximum likelihood decision by a maximum likelihood sequence estimator for desired and inter-channel interference signals under inter-symbol interference generating environment.
For example, W. Van Etten has proposed, as a maximum likelihood sequence estimator, a receiver using the Viterbi algorithm (W. Van Etten, "Maximum Likelihood Receiver for Multiple Channel Transmission System," IEEE Trans. on Comm., February 1976). However, this receiver is based on the assumption that the value of the impulse response of the transmission line is preknown. A receiver of the type that estimates transmission line parameters and employs a maximum likelihood sequence estimator has been proposed by Howard E. Nicols, Arithur A. Giordano and John G. Proakis. According to their proposal, the transmission line parameters are estimated and updated by an adaptation algorithm through use of an estimated value for a symbol detection which is outputted from the maximum likelihood sequence estimator after being delayed for the same period of time as a received signal sample delayed for a fixed period of time. This receiver operates well when the radio channel undergoes relatively slow time-variations. In the mobile radio channel, however, since the amplitudes and phases of desired and interference signals varyat high speed, the estimated value of the received signal sample which is delayed for a fixed period of time, as proposed by Howard E. Nicols, Arithur A. Giordano and John G. Proakis, is no longer a current estimated value, and the transmission characteristic is seriously degraded.
To improve the characteristic of an adaptive equalizer based on the maximum likelihood sequence estimation scheme, A. P. Clark, J. D. Harvey and J. P. Driscoll have proposed a Near-Maximum-Likelihood detection scheme as a solution to the poor estimation of the transmission line parameters due to the fixed delay of the received signal sample which poses a serious problem in the adaptive maximum likelihood receiver using the maximum likelihood sequence estimation scheme (A. P. Clark, J. D. Harvey and J. P. Driscoll, "Near-Maximum-Likelihood detection processes for distorted digital signals," Radio & Electronics Engineer, Vol. 48, No. 6, pp. 301-309). Moreover, A. P. Clark has proposed an FDM (Frequency Division Multiplexing) system which transmits two signals over the same frequency channel through utilization of the Near-Maximum-Likelihood detection scheme (U.S. Pat. No. 4,862,483). In this system, however, transmission signal sequence candidates (First Vector) to be stored in a memory and sets of transmission line parameters (vectors) corresponding to them are large in number and extended received signal sequence candidates (Second Vector) are sequentially chosen in decreasing order of likelihood as new transmission signal sequence candidates. Consequently, when the likelihood of the transmission signal sequence candidate (First Vector) of the highest likelihood is far higher than the likelihood of the other transmission signal sequence candidates (First Vector), the order of likelihood of the extended signal sequence candidates (Second Vector) is dependent upon the likelihood of the First Vector; hence, there is substantially no possibility of other First Vectors being chosen and no maximum likelihood detection takes place.
On the other hand, Fukawa and Suzuki have proposed, in "Adaptive Equalization with RLS-MLSE for Frequency-Selective Fast Fading Mobile Radio Channels," IEEE Globecom'91, Dec. 2-5, 1991 or "Recursire Least-Squares Adaptive Algorithm Maximum-Likelihood Sequence Estimation with Higher-Order State Variable Model of Radio Channels-Adaptive Performance Improvement of RLS-MLSE-" Journal of the Institute of Electronics, Information and Communication Engineers of Japan, B-II, Vol. J75, No. 7, 1992, a transmission parameter estimation scheme suitable for the maximum likelihood sequence estimation which keeps high-speed, precise track of the fast fading or fast-changing mobile radio channel. An equalizer of this scheme cancels inter-symbol interference but does not cancel co-channel interference, and hence possesses a defect that it does not operate under the co-channel interference environment of high signal level.
The present invention applies the above-mentioned transmission line parameter estimation scheme to an interference canceller using the maximum likelihood sequence estimator and, through utilization of the fading property of the mobile radio channel that the amplitudes and phases of desired and interference signals vary at high speed independently of each other, permits efficient separation of the signals and accurate estimation of the transmission line parameters for the both signals.
A description will be given first, with reference to FIG. 1, of a conventional receiver of the maximum likelihood sequence estimation scheme that has the above-said adaptive equalization feature.
This receiver is made up of: a desired signal estimation part 10 which estimates and outputs a desired signal to be received; an error estimation part 30 which subtracts an estimated received signal Y.sub.m (n) from the desired signal estimation part 10 from a received signal y(n) sampled after being synchronously detected, which is input into a terminal IN with a period T (a signal which is obtained by sub-synchronously detecting the received signal and sampling the detected output and is usually represented by a complex signal which has an in-phase component I and an orthogonal component Q as its real and imaginary parts, respectively) and outputs an estimation error signal .epsilon.; a state estimation part 40 which calculates the likelihood from the estimation error signal .epsilon. to make a maximum likelihood sequence estimation; and a transmission line or channel parameter estimation part 50 which controls the conversion or transformation parameter of the desired signal estimation part 10 on the basis of the output from the state estimation part 40 and the estimation error signal .epsilon..
The desired signal estimation part 10 is formed by a transversal filter 11. For example, in a single delay stage, the transversal filter 11 comprises, as shown in FIG. 2A, a delay element 111 of a delay time equal to the sample period T, multipliers 112 and 113 connected to its input and output, respectively, and an adder 114 which adds together the outputs from the multipliers 112 and 113. The state estimation part 40 supplies the transversal filter 11 with estimated transmitted signal sequences a.sub.m (n) and a.sub.m (n-1) composed of complex symbols corresponding to the current point nT and a point one sample before, respectively, which are multiplied in the multipliers 112 and 113 by tap coefficients h.sub.m0 and h.sub.m1, respectively. The multiplied outputs are added by the adder 114, whose output is applied as the estimated received signal y.sub.m (n) to the error estimation part 30 shown in FIG. 1. The tap coefficients h.sub.m0 and h.sub.m1 of the transversal filter 11 can adaptively be changed by a tap coefficient control part 51 in accordance with the time-varying channel impulse response. The error estimation part 30 subtracts, by an adder 31, the estimated received signal Y.sub.m (n) from the desired signal estimation part 10 from the received signal y(n) fed to the terminal IN and outputs the estimation error signal .epsilon.. When the received signal does not contain interference components from other stations, the estimation error signal .epsilon. is composed of a noise component alone. The estimation error signal .epsilon. is fed to a likelihood calculation part 41, wherein it is converted to a likelihood signal.
The likelihood calculation part 41 may be formed by a square multiplier which squares the estimation error .epsilon.. The likelihood signal -.vertline..epsilon..vertline..sup.2 is input into a maximum likelihood sequence estimator 42. When a square multiplier is used as the likelihood calculation part 41, the minimum output from the square multiplier provides the maximum likelihood. The likelihood signal is fed to the maximum likelihood sequence estimator 42 to estimate the transmitted signal sequence.
The maximum likelihood sequence estimator 42 generates and outputs one state sequence candidate vector for the sequence of sequential transition of the received signal. Next, a modulation signal generating part 44 modulates the candidate vector into a transmission signal sequence candidate vector (a vector using a complex symbol) and sends it to the desired signal estimation part 10. The estimation error signal e corresponding to this transmitted signal sequence candidate vector is fed to the state estimation part 40 and converted by the likelihood calculation part 41 into a likelihood signal. Then, another state sequence candidate vector is generated and the corresponding likelihood signal is produced following the same procedure as described above. Thus, the same processing is repeated to obtain the likelihood signal for each possible state sequence candidate. By this, a likelihood signal (called a branch metric) -.vertline..epsilon..vertline..sup.2 is provided for every branch possible of transition from each state S.sub.j (n) (j=0, 1, . . . ,M-1) at time nT to each state S.sub.j (n+1) at time (n+1)T. By repeating such processing from time nT to (n+ G-1) T, the likelihood signal (i.e. the branch metric) -.vertline..epsilon..vertline..sup.2 is obtained for each branch corresponding to each transition. Assuming, for example, that each state S.sub.j (n) at time nT is possible of transition to any of M states at the next time (n+1)T, a total of M.sup.G paths exist which are possible for the transition of an arbitrary one of the M states at time nT to an arbitrary one of the M states at time (N+G-1). According to the maximum likelihood sequence estimation scheme, an accumulated value (corresponding to a path metric) of the likelihood signal -.vertline..epsilon..vertline..sup.2 (corresponding to the branch metric) of the branch between two successive points in time is calculated every path for transition and that one of these M.sup.G paths which has the largest path metric (or smallest accumulated value of the square error .vertline..epsilon..vertline..sup.2) is estimated to be the state transition sequence of the transmitted signal. Since the state transition sequence corresponds to the signal sequence, it is possible to use the estimated state transition sequence to make a decision of the received signal sequence. The decision output is provided at an output terminal OUTd in FIG. 1.
Such a received signal decision is made by selecting that one of the sequences corresponding to the G input samples which provides the highest likelihood. With a large value set for G, the probability of being the estimated state sequence being correct is high and the maximum likelihood detection is provided, but since the total number M.sup.G of paths used increases exponentially, the total amount of processing required also increased exponentially. On the other hand, a small value for G decreases the total amount of processing involved but lessens the probability of the estimated state sequence being correct. With the Viterbi algorithm which is one of the maximum likelihood sequence estimation schemes, the branch metrics of M branches for transition from each state at the immediately preceding point in time are calculated for each state at each time point and the branch metrics thus calculated are added respective path metrics until the immediately preceding time; then, that one of the paths which has the largest path metric (i.e. the highest likelihood) is selected and the remaining paths are discarded. In this way, the Viterbi algorithm reduces the total amount of processing involved.
Incidentally, according to the maximum likelihood sequence estimation scheme which is known as a signal decision scheme, upon each input of the signal sample value y(n), a new path metric is calculated for each state S.sub.j (n) (j=1, 0, . . . ,M-1) as described above, then that one of the paths which provides the highest likelihood (or the largest path metric) is decided to be the path used for signal transition and a signal decision value is produced calculating the path metric in the past.
The tap coefficient control part 51 comprises, as shown in FIG. 2B, a tap coefficient storage part 511, a tap coefficient change-over switch 512 and a tap coefficient updating part 513. The tap coefficient storage part 511 is a circuit which stores sets of tap coefficients (tap coefficient vectors) corresponding to respective states. The tap coefficient change-over switch 512 selects from the tap coefficient storage part 511 the tap coefficient vector corresponding to each state and feeds it to the transversal filter 11. Upon completion of updating the path metric for each state in the maximum likelihood sequence estimator 42, the tap coefficient updating part 513 updates the plurality of sets of tap coefficients (a plurality of tap coefficient vectors) stored in the tap coefficient storage part 511 in correspondence to the respective states. The updating of the tap coefficients is performed using the signal sequence outputted from the state estimation part 40 and the estimation error signal e from the error estimation part 30. This updating is carried out, by a known RLS, LMS (Least Mean-Square) or similar adaptation algorithm, for each tap coefficient vector corresponding to each state so that the square .vertline..epsilon..vertline..sup.2 of the estimation error signal is reduced. Consequently, the thus updated tap coefficient vector for each state reflects the current channel impulse response; hence, when the radio channel moves at high speed with time due to fading as in mobile radio communication, the channel tracking property improves, providing an excellent receiving characteristic.
The maximum likelihood sequence estimation (MLSE) scheme, which is used as a signal decision scheme, is an estimation scheme that calculates the likelihood for all possible complex symbol sequence candidates and selects, as the signal decision value, that one of the complex symbol sequence candidates which provides the highest likelihood. As the complex symbol sequence becomes longer, the number of possible sequences increases exponentially; it is therefore a general practice in the art to use a state estimation scheme which reduces the number of sequences and hence suppresses the total amount of processing through utilization of the Viterbi algorithm.
In an adaptive equalizer which is used to cancel a channel distortion by known multi-path propagation, a delay wave (letting its maximum delay time be an Ld symbol time) is taken into account. In this case, however, assume that no co-channel interference signal exists.
Representing the complex symbol at time t=nT by a(n), the state S(n) at time t=nT is defined by a sequence of values of immediately preceding Ld selected complex symbol candidates and is expressed by the following equation. EQU S(n)={a(n-1), a(n-2), . . . , a(n-Ld+1), a(n-Ld)} (1)
Here, in the case of an M-ary signaling modulation system, the complex symbol candidates a(n-1), a(n-2), . . . , a(n-Ld+1), a(n-Ld) each take one of M complex symbols Cp (0.ltoreq.p.ltoreq.M-1). The complex symbol herein mentioned represents a signal whose in-phase and quadrature-phase components I and Q correspond to real and imaginary parts, respectively. Accordingly, the total number of states S(n) at time t=nT is M.sup.Ld. For example, in the case of BPSK signaling modulation, the complex symbol Cp is as follows: ##EQU1## Hence, the total number of states is 2.sup.Ld. In the case of QPSK signaling modulation, the complex symbol Cp is as follows: ##EQU2## where j is the imaginary unit, and the total number of states is 4.sup.Ld.
To cancel the delay signal component (a multi-path component) having propagated through a different transmission path, a delay of about one- or two-symbol time needs only to be considered in practice, though dependent on the symbol rate of the transmitted signal; hence, one or two delay stages of the transversal filter in actual receivers, for instance, produces the intended effect. The transversal filter 11 with one delay stage (Ld=1) and an m-th transmitted symbol sequence candidate {a.sub.m (n-1), a.sub.m (n)} which is provided to the filter are such as referred to previously with respect to FIG. 2A.
FIG. 2C is a state transition trellis diagram in the case of QPSK modulation. In the case of one delay stage (one-symbol delay), the number of states at each point in time is 4.sup.Ld =4.sup.1 =4 and transition is allowed to each state from any state at the immediately preceding time. Now, let a j-th state at time nT be represented by S.sub.j (n), where 0.ltoreq.j.ltoreq.3, and when time elapses from nT to (n+1)T, the state transition occurs. In this instance, each state at time nT is possible of transition to any states at time (n+1)T; hence, one state is allowed to transition to four states. As shown in FIG. 2C, each state branches into four states, which, in turn, merge into one state. To select one of four merging transitions, the Viterbi algorithm uses the path metric J.sub.c [S.sub.j '(n+1), S.sub.j (n)] of a path for transition to the state S.sub.j '(n+1) via the state S.sub.j (n).
The path metric J.sub.c [S.sub.j '(n+1), S.sub.j (n)] of the path for transition to the state S.sub.j '(n+1) via the state S.sub.j (n) is computed by the following equation, using a branch metric .LAMBDA.[S.sub.j '(n+1), S.sub.j (n)]. EQU J.sub.c [S.sub.j '(n+1), S.sub.j (n)]=J[S.sub.j (n)]+.LAMBDA.[S.sub.j '(n+1), S.sub.j (n)] (2)
In the above, EQU .LAMBDA.[S.sub.j '(n+1), S.sub.j (n)]=-.vertline..epsilon..sub.m (n).vertline..sup.2 ( 3)
where .epsilon..sub.m (n) is an estimation error expressed by .epsilon..sub.m (n)=y(n)-y.sub.m (n) and J[S.sub.j (n)] is the path metric surviving at the state S.sub.j (n) at time nT, which corresponds to the likelihood. The m-th complex symbol sequence candidate in the state transition from S.sub.j (n) to S.sub.j '(n+1) is expressed by {a.sub.m (n), a.sub.m (n+1)}. In the Viterbi algorithm, ##EQU3## where MAX represents the maximum value when the value j is changed from 0 to 3. Equation (2)' is used to select the path metric J[S.sub.j '(n+1)] at time (n+1)T. Letting the value j which provides the maximum value in the right side of Eq. (2)' be represented by j.sub.max, the path that survives in this case is a path which reaches the state S.sub.j ' (n+1) via Sj.sub.max. Repeating this operation thereafter, paths of the same number as the states survive at each point in time. These paths are called survivors. Incidentally, because of the limited space of the memory used, the time sequence of states that are stored usually goes back only to previous time (D-Ld+1)T (where D.gtoreq.Ld and T is the symbol period); when the remaining paths do not merge at the previous time (D-Ld+1)T, the signal decision is made going back by a period DT on the basis of the path which has the maximum likelihood or the largest path metric at the current point in time. The signal that is decided in this case is a signal delayed by DT relative to the current point, and DT is called a decision delay time (G. Ungerboeck, "Adaptive maximum likelihood receiver for carrier-modulated data-transmission systems," IEEE Trans. Commun., vol. COM-22, pp. 624-636, 1974).
Incidentally, in the receiver using the maximum likelihood sequence estimator with the adaptive equalization feature, the error estimation part 30 in FIG. 1 calculates the difference between the received signal and the desired signal estimated for reception to obtain an estimation error signal and the likelihood calculation part 41 calculates the likelihood. Hence, when an interference signal, such as an inter-symbol interference signal, is caused by a delayed signal of its own, its replica is generated which can be used to cancel the influence of the inter-symbol interference. When the received signal contains an interference signal from another station, the interference signal component still remains in an estimation error signal and at the output of the likelihood calculation part 41 it is regarded as similar to noise--this presents a problem that the receiving performance is seriously impaired. In mobile radio communication in which each cell may sometimes receive a co-channel interference signal from an adjacent cell, in particular, there is a strong demand for cancelling the influence of the interference signal.